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Provenance management must be present to enhance the overall security andreliability of long-tail microscopy (LTM) data management systems. However,there are challenges in provenance for domains with LTM data. The provenancedata need to be collected more frequently, which increases system overheads (interms of computation and storage) and results in scalability issues. Moreover,in most scientific application domains a provenance solution must considernetwork-related events as well. Therefore, provenance data in LTM datamanagement systems are highly diverse and must be organized and processedcarefully. In this paper, we introduce a novel provenance service, calledProvLet, to collect, distribute, analyze, and visualize provenance data in LTMdata management systems. This means (1) we address how to filter and store thedesired transactions on disk; (2) we consider a data organization model athigher level data abstractions, suitable for step-by-step scientificexperiments, such as datasets and collections, and develop provenancealgorithms over these data abstractions, rather than solutions consideringlow-level abstractions such as files and folders. (3) We utilize ProvLet's logfiles and visualize provenance information for further forensics explorations.The validation of ProvLet with actual long tail microscopy data, collected overa period of six years, shows a provenance service that yields a low systemoverhead and enables scalability.

ProvLet: A Provenance Management Service for Long Tail Microscopy Data